Multivariate adaptive regression spline ensembles for management of multilayered coastal aquifers

Application of multivariate adaptive regression spline ensembles (En-MARS) in a coupled simulation-optimization methodology to derive multiple-objective optimal groundwater extraction strategies for a multilayered coastal aquifer system is demonstrated. Two conflicting objectives of groundwater extraction strategies are solved using a controlled elitist multiobjective genetic algorithm. A three-dimensional density-dependent coupled flow and salt-transport numerical simulation model is used to generate the training patterns of groundwater extraction strategies and resulting saltwater concentrations. Prediction capability of En-MARS is compared with that of the best multivariate adaptive regression spline (MARS) model in the ensemble. En-MARS is then linked externally within the optimization algorithm to develop the management model. The optimal solutions obtained from the En-MARS models are verified by running the numerical simulation model. The results indicate that MARS-based ensemble modeling approach is able to provide reliable solutions for a multilayered coastal aquifer management problem. The adaptive nature of MARS models and use of ensembles and parallel processing results in a computationally efficient, accurate, and reliable methodology for coastal aquifer management that also incorporates uncertainties in modeling. (C) 2017 American Society of Civil Engineers.

Journal of Hydrologic Engineering

Journal of Hydrologic Engineering

22

1943-5584

N/A

9

13